This study will examine the development of EEG sleep state in the "healthy" low-birthweight neonate (less than 1500 grams) until a corrected term age. Maturational similarities and differences in neurophysiological development will be assessed between pre- term infants of increasing gestational ages up to term as compared to a control group of appropriate-for-gestational age term infants. Applying the principles of biological rhythms to the developing brain, EEG sleep state will be investigated along with rapid eye movements (REM's), motility and arousal. These signals will be analyzed both visually and with computer assistance. Three specific hypotheses will be examined: 1) EEG sleep organization in healthy pre-term neonates will have a predictive pattern of development as they correct to a full-term age; 2) EEG sleep state, motility and arousal in premature neonates corrected to full-term age will have differences from normal control full- term infants with respect to a shorter sleep cycle, increased number and types of body movements and increased number of arousals; 3) Relationships between ultradian (high-frequency) rhythms of neonatal EEG sleep and the physiologic parameters of REM, arousal and motility are established by a corrected term age, and can be expressed by computer analysis in terms of coalescence and periodicity. The establishment of normative EEG-sleep data for the "healthy" premature neonate over a developmental time span is essential before accurate comparisons can be made with sick neonates who are at risk for neurologic sequelae. The study will be carried out using monitoring systems that perform prolonged recordings in the Neonatal Intensive Care Unit. These systems include both synchronized video-EEG monitoring and bedside application of computer equipment for data reduction and analysis. This will be done without disruption of the patient's medical care. Specific training objectives are presented for learning theoretical principles of biological rhythms for both the mature and developing organism, and acquiring skills in computer-assisted analysis of EEG sleep and other physiologic signals.